The present invention seeks to provide improved injection moulding of plastics products.
Currently, a skilled operator of an injection moulding machine sets the processing conditions and makes alterations if necessary (e.g. due to batch to batch variations in the raw material, or change in room temperature or humidity). The need to make changes is identified by inspection (visual as well as from limited off non-automatic line testing).
The cost of labour to carry out the alterations and the inspection is a problem. In addition, scrap still occurs (e.g. 5% would not be atypical), so the current technique of manual adjustment is far from perfect. Some materials are particularly difficult to process (e.g. recycled PVC) and would benefit from more careful control. Also cycle times are not always optimised which results in higher unit costs than necessary.
There is a requirement in the industry to reduce time to market for new products and to increase the production efficiency of injection moulded products through optimisation of cycle times. The need to process recycled material is also growing. Recycled materials where the polymer grade is not known or is not consistent produce quality control problems.
The present invention seeks to provide improved injection moulding. Controlling the injection moulding process using in-process measurements ensures the high quality of products. Furthermore, continuous monitoring of process conditions and materials properties can lead to reduced costs and higher levels of manufacturing efficiency, thereby improving industrial competitiveness.
The present invention further seeks to provide a system for automatically determining optimum injection moulding conditions.
According to a first aspect of the present invention there is provided a method of controlling a process for manufacturing injection moulded articles using an injection moulding machine including testing predetermined physical properties of selected articles; determining predetermined properties of the process; and adjusting the process parameters set on the injection moulding machine to achieve pre-set values of the physical properties of the articles for subsequent articles manufactured by the process.
Preferably, the testing the selected articles is carried out during the process within a predetermined number of cycles. The testing the selected articles may be carried out by an automated system.
Advantageously, the testing step includes testing for one or more of the dimensions, weight, gloss, colour, hardness, stiffness and impact resistance of the removed articles and the determining step includes obtaining one or more of hydraulic pressure, nozzle temperature, nozzle pressure, nozzle pressure drop. The determining step may include in situ real time determination of the viscosity of the injection material.
Preferably, the determining step includes obtaining automatically one or more of the following in-line process measurement points:
a) position of screw/piston at the beginning of an injection phase;
b) nozzle pressure at the end of a filling phase;
c) position of the screw at the end of the filling phase;
d) maximum nozzle pressure at the end of the injection phase;
e) screw position at a cross-over point where the process changes from keeping screw/piston velocity constant to keeping hydraulic pressure constant;
f) nozzle pressure at the cross-over point;
g) nozzle pressure integral during one cycle; and
h) position of the screw at the end of the holding phase.
The tested physical properties of the selected articles are advantageously stored in memory together with a machine cycle count number.
In the preferred embodiment, the adjusting step includes using one or more process optimisers for setting the injection moulding process. The optimisers may be artificial intelligence programs and may include one or more of: a) a case based reasoning optimiser; b) a fuzzy optimiser; and c) a rule based reasoning optimiser.
The case based reasoning optimiser preferably uses previous removed article data and correction data and measurements of subsequently removed tested articles to determine the most appropriate adjustments.
The adjusting step preferably makes adjustments to the process on the basis of confidence coefficients associated with each possible adjustment suggested by the optimisers. In an embodiment, the confidence coefficients are determined on the basis of the effect of previous adjustments.
According to a second aspect of the present invention there is provided an injection moulding system comprising: an injection moulding machine; a data collector; a process controller; wherein means are provided for supplying process data from the injection moulding machine to the data collector where it is compiled with product data and wherein the process controller includes means for evaluating the process and product data and altering the settings of the injection moulding machine.
Preferably, the system includes automated product data collection means which provides the product data to the data collector.
Advantageously, the injection moulding machine includes a nozzle with temperature and pressure sensors which obtain process data.
The nozzle preferably has a bore with a constriction and two pressure sensors disposed at either end of the constriction to provide pressure difference data. The pressure difference data together with the screw/piston speed, the cross-section of the bore and the distance between the pressure sensors provide a measurement of the viscosity of the fluid being moulded.
The preferred embodiment can automatically optimise the settings of injection moulding machines using automatic measurement of processing parameters (such as viscosity), product properties (such as weight, gloss and dimensions) and novel artificial intelligence computing software.
Such automatic control of the injection moulding machine can produce products within specification even when the raw material and/or room temperature/humidity/dust levels are changing, or when optimum processing conditions are not known, e.g. when the product is being moulded for the first time.
In a practical embodiment, an injection moulding system optimises the injection moulding process by removing selected articles from a plurality of articles produced cyclically and tests a plurality of physical properties of the removed articles including dimensions, weight and gloss, together with a plurality of process parameters. Adjustment of the process is made automatically on the basis of three optimisers, including a case based reasoning optimiser, a fuzzy optimiser and a rule based reasoning optimiser, in order to achieve pre-set article properties. The optimisers have confidence factors associated therewith, determined on the effect of previous process adjustments.